# Note to save the Plots remove the # from the postscript() and dev.off() lines
library('tidyverse')
## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag(): dplyr, stats
theme_set(theme_minimal())
Seqdir="~/Box Sync/SeqPaperData/TJ12/TJ12_collapse12" # remote directory where data is
outdir<-"~/Box Sync/SeqPaperData/TJ12/motif/cell4_mature"
# the SampleSummaryFile is a temporary file needed.
project="TJ12"
#
setwd(Seqdir)
# > head(dir(),14)
# [1] "4N01_ccell4_500ng_1_genomic_count12_seqs.txt" "4N01_ccell4_500ng_1_genomic_count12.txt"
# [3] "4N01_ccell4_500ng_1_Hairpin_count12_seqs.txt" "4N01_ccell4_500ng_1_Hairpin_count12.txt"
# [5] "4N01_ccell4_500ng_1_Libcontam_count12_seqs.txt" "4N01_ccell4_500ng_1_Libcontam_count12.txt"
# [7] "4N01_ccell4_500ng_1_Mature_count12_seqs.txt" "4N01_ccell4_500ng_1_Mature_count12.txt"
# [9] "4N01_ccell4_500ng_1_ribo_count12_seqs.txt" "4N01_ccell4_500ng_1_ribo_count12.txt"
# [11] "4N01_ccell4_500ng_1_svrna_count12_seqs.txt" "4N01_ccell4_500ng_1_svrna_count12.txt"
# [13] "4N01_ccell4_500ng_1_trna_count12_seqs.txt" "4N01_ccell4_500ng_1_trna_count12.txt"
# >
# filelist=dir()
library(stringr)
# library(dplyr)
# filename=filelist[seq(1,length(filelist), 50)] # if you don't want to analyze all, choose subset here
# use this for looping
#
filename="4N01_ccell4_500ng_1_Mature_count12_seqs.txt" #
infoFile=read.table(filename, header=FALSE, stringsAsFactors = FALSE)
# $1=HairpinName, $2=leftSeq/insert, $3=AdapterMatch, $4=WholeSeq, $5=4N-miRNA-4Nbarcode4N
names(infoFile) <- c("miRNA","Insert","AdapterMatch","WholeSeq","TrimmedSeq")
# library(seqLogo)
# library(Biostrings)
#
# library("devtools")
# install_github("omarwagih/ggseqlogo")
require(ggplot2)
require(ggseqlogo)
## Loading required package: ggseqlogo
Sample <- infoFile %>% mutate(FiveP4nt = (str_sub(infoFile$Insert, 1, 4)), ThreePUMI=(str_sub(AdapterMatch, 1, 13)))
setwd(outdir)
Sample_5pAll <- Sample %>% select(FiveP4nt)
seqsAll_5p <- as.character(Sample_5pAll[!grepl("N",Sample_5pAll$FiveP4nt),])
# save file
postscript(file="Figure2_5p.eps",paper = "special", width=1.25, height=1.5, horizontal=FALSE)
ggplot() + geom_logo(seqsAll_5p, method='p') + theme_logo(base_size = 8, base_family = "") #
dev.off()
## quartz_off_screen
## 2
Sample_3pAll <- Sample %>% select(ThreePUMI)
seqsAll_3p <- as.character(Sample_3pAll[!grepl("N",Sample_3pAll$ThreePUMI),])
postscript(file="Figure2_3p.eps",paper = "special", width=3, height=1.5, horizontal = FALSE)
# par(mar=c(5,3,2,2)+0.1)
# seqLogo(consensusMatrix(seqsAll_3p, as.prob = T)[1:4,], ic.scale=F)#
ggplot() + geom_logo(seqsAll_3p, method='p') + theme_logo(base_size = 8, base_family = "")
dev.off()
## quartz_off_screen
## 2
#
# now find motifs for top 25 expressed mature miRNAs
# open counts file
countsdir<-"~/Box Sync/SeqPaperData/TJ12/TJ12_collapse12"
setwd(countsdir)
# Countsfilelist=dir()
filename2="4N01_ccell4_500ng_1_Mature_count12.txt" #
# filename2=Countsfilelist[seq(1,length(filelist), 50)]
countsfile <- read.table(filename2, header=FALSE, stringsAsFactors = FALSE)
countsfile <- arrange(countsfile, desc(V2))
top25mirs <- countsfile[1:25,1]
#
# > top25mirs
# [1] "hsa-miR-21-5p" "hsa-miR-24-3p" "hsa-miR-29a-3p" "hsa-let-7a-5p" "hsa-miR-27a-3p"
# [6] "hsa-miR-100-5p" "hsa-miR-29b-3p" "hsa-miR-125b-5p" "hsa-miR-16-5p" "hsa-miR-191-5p"
# [11] "hsa-let-7f-5p" "hsa-miR-22-3p" "hsa-miR-103b" "hsa-miR-23a-3p" "hsa-miR-96-5p"
# [16] "hsa-let-7b-5p" "hsa-miR-26a-5p" "hsa-miR-221-3p" "hsa-let-7i-5p" "hsa-miR-320a"
# [21] "hsa-let-7g-5p" "hsa-miR-3074-5p" "hsa-miR-15a-5p" "hsa-miR-103a-3p" "hsa-miR-424-5p" setwd(outdir)
setwd(outdir)
for (mir in top25mirs){
Sample_mir5p <- Sample %>% filter(miRNA==mir) %>% select(FiveP4nt)
seqs_mir_5p <- as.character(Sample_mir5p[!grepl("N",Sample_mir5p$FiveP4nt),])
#
motif_file <- paste(mir,"_5p.eps", sep="")
# save file
postscript(file=motif_file,paper = "special", width=1.5, height=1.5, horizontal = FALSE)
print(ggplot() + geom_logo( seqs_mir_5p, method='p') + theme_logo(base_size = 8, base_family = ""))
dev.off()
Sample_mir3p <- Sample %>% filter(miRNA==mir) %>% select(ThreePUMI)
seqs_mir_3p <- as.character(Sample_mir3p[!grepl("N",Sample_mir3p$ThreePUMI),])
motif_file2 <- paste(mir,"_3p.eps", sep="")
postscript(file=motif_file2, paper = "special", width=3, height=1.5, horizontal = FALSE)
print(ggplot() + geom_logo( seqs_mir_3p, method='p') + theme_logo(base_size = 8, base_family = ""))
dev.off()
}
# now look for the highest one in serum, which is low in the cells
mir = "hsa-miR-451a"
Sample_mir5p <- Sample %>% filter(miRNA==mir) %>% select(FiveP4nt)
seqs_mir_5p <- as.character(Sample_mir5p[!grepl("N",Sample_mir5p$FiveP4nt),])
motif_file <- paste(mir,"_5p_2.eps", sep="")
pt= ggplot() + geom_logo( seqs_mir_5p, method='p') + theme_logo(base_size = 8, base_family = "")
# save file
postscript(file=motif_file,paper = "special", width=1.5, height=1.5, horizontal = FALSE)
print(pt)
dev.off()
## quartz_off_screen
## 2
Sample_mir3p <- Sample %>% filter(miRNA==mir) %>% select(ThreePUMI)
seqs_mir_3p <- as.character(Sample_mir3p[!grepl("N",Sample_mir3p$ThreePUMI),])
motif_file2 <- paste(mir,"_3p_2.eps", sep="")
# save file
pt2=ggplot() + geom_logo( seqs_mir_3p, method='p') + theme_logo(base_size = 8, base_family = "")
postscript(file=motif_file2, paper = "special", width=3, height=1.5, horizontal = FALSE)
print(pt2)
dev.off()
## quartz_off_screen
## 2
# ccell 4 #2
setwd(Seqdir)
outdir<-"~/Box Sync/SeqPaperData/TJ12/motif/cell4_mature2/"
filename="4N02_ccell4_500ng_2_Mature_count12_seqs.txt"
infoFile=read.table(filename, header=FALSE, stringsAsFactors = FALSE)
# $1=HairpinName, $2=leftSeq/insert, $3=AdapterMatch, $4=WholeSeq, $5=4N-miRNA-4Nbarcode4N
names(infoFile) <- c("miRNA","Insert","AdapterMatch","WholeSeq","TrimmedSeq")
Sample <- infoFile %>% mutate(FiveP4nt = (str_sub(infoFile$Insert, 1, 4)), ThreePUMI=(str_sub(AdapterMatch, 1, 13)))
#
Sample_5pAll <- Sample %>% select(FiveP4nt)
seqsAll_5p <- as.character(Sample_5pAll[!grepl("N",Sample_5pAll$FiveP4nt),])
setwd(outdir)
# save file
postscript(file="Figure2_5p_2.eps",paper = "special", width=1.25, height=1.5, horizontal=FALSE)
print(ggplot() + geom_logo(seqsAll_5p, method='p') + theme_logo(base_size = 8, base_family = "")) #
dev.off()
## quartz_off_screen
## 2
Sample_3pAll <- Sample %>% select(ThreePUMI)
seqsAll_3p <- as.character(Sample_3pAll[!grepl("N",Sample_3pAll$ThreePUMI),])
postscript(file="Figure2_3p_2.eps",paper = "special", width=3, height=1.5, horizontal = FALSE)
print(ggplot() + geom_logo(seqsAll_3p, method='p') + theme_logo(base_size = 8, base_family = ""))
dev.off()
## quartz_off_screen
## 2
#
#
# now find motifs for top 25 expressed mature miRNAs
# open counts file
countsdir<-"~/Box Sync/SeqPaperData/TJ12/TJ12_collapse12"
setwd(countsdir)
# Countsfilelist=dir()
filename2="4N02_ccell4_500ng_2_Mature_count12.txt" #
# filename2=Countsfilelist[seq(1,length(filelist), 50)]
countsfile <- read.table(filename2, header=FALSE, stringsAsFactors = FALSE)
countsfile <- arrange(countsfile, desc(V2))
top25mirs <- countsfile[1:25,1]
#
# > top25mirs
# [1] "hsa-miR-21-5p" "hsa-miR-24-3p" "hsa-let-7a-5p" "hsa-miR-27a-3p" "hsa-miR-100-5p"
# [6] "hsa-miR-191-5p" "hsa-miR-29a-3p" "hsa-miR-16-5p" "hsa-miR-29b-3p" "hsa-miR-125b-5p"
# [11] "hsa-let-7i-5p" "hsa-miR-103b" "hsa-miR-26a-5p" "hsa-let-7f-5p" "hsa-miR-22-3p"
# [16] "hsa-miR-3074-5p" "hsa-miR-96-5p" "hsa-miR-221-3p" "hsa-let-7g-5p" "hsa-miR-23a-3p"
# [21] "hsa-miR-93-5p" "hsa-miR-320a" "hsa-let-7b-5p" "hsa-miR-103a-3p" "hsa-miR-17-5p"
setwd(outdir)
for (mir in top25mirs) {
Sample_mir5p <- Sample %>% filter(miRNA==mir) %>% select(FiveP4nt)
seqs_mir_5p <- as.character(Sample_mir5p[!grepl("N",Sample_mir5p$FiveP4nt),])
motif_file <- paste(mir,"_5p_2.eps", sep="")
pt= ggplot() + geom_logo( seqs_mir_5p, method='p') + theme_logo(base_size = 8, base_family = "")
# save file
postscript(file=motif_file,paper = "special", width=1.5, height=1.5, horizontal = FALSE)
print(pt)
dev.off()
Sample_mir3p <- Sample %>% filter(miRNA==mir) %>% select(ThreePUMI)
seqs_mir_3p <- as.character(Sample_mir3p[!grepl("N",Sample_mir3p$ThreePUMI),])
motif_file2 <- paste(mir,"_3p_2.eps", sep="")
# save file
pt2=ggplot() + geom_logo( seqs_mir_3p, method='p') + theme_logo(base_size = 8, base_family = "")
postscript(file=motif_file2, paper = "special", width=3, height=1.5, horizontal = FALSE)
print(pt2)
dev.off()
}
# now look for the highest one in serum, which is low in the cells
mir = "hsa-miR-451a"
Sample_mir5p <- Sample %>% filter(miRNA==mir) %>% select(FiveP4nt)
seqs_mir_5p <- as.character(Sample_mir5p[!grepl("N",Sample_mir5p$FiveP4nt),])
motif_file <- paste(mir,"_5p_2.eps", sep="")
pt= ggplot() + geom_logo( seqs_mir_5p, method='p') + theme_logo(base_size = 8, base_family = "")
# save file
postscript(file=motif_file,paper = "special", width=1.5, height=1.5, horizontal = FALSE)
print(pt)
dev.off()
## quartz_off_screen
## 2
Sample_mir3p <- Sample %>% filter(miRNA==mir) %>% select(ThreePUMI)
seqs_mir_3p <- as.character(Sample_mir3p[!grepl("N",Sample_mir3p$ThreePUMI),])
motif_file2 <- paste(mir,"_3p_2.eps", sep="")
# save file
pt2=ggplot() + geom_logo( seqs_mir_3p, method='p') + theme_logo(base_size = 8, base_family = "")
postscript(file=motif_file2, paper = "special", width=3, height=1.5, horizontal = FALSE)
print(pt2)
dev.off()
## quartz_off_screen
## 2
# serum CB2_1f1
setwd(Seqdir)
outdir<-"~/Box Sync/SeqPaperData/TJ12/motif/CB2_1f1_mature/"
filename="4N23_CB2_1f_1_Mature_count12_seqs.txt"
infoFile=read.table(filename, header=FALSE, stringsAsFactors = FALSE)
# $1=HairpinName, $2=leftSeq/insert, $3=AdapterMatch, $4=WholeSeq, $5=4N-miRNA-4Nbarcode4N
names(infoFile) <- c("miRNA","Insert","AdapterMatch","WholeSeq","TrimmedSeq")
#
Sample <- infoFile %>% mutate(FiveP4nt = (str_sub(infoFile$Insert, 1, 4)), ThreePUMI=(str_sub(AdapterMatch, 1, 13)))
Sample_5pAll <- Sample %>% select(FiveP4nt)
seqsAll_5p <- as.character(Sample_5pAll[!grepl("N",Sample_5pAll$FiveP4nt),])
setwd(outdir)
# save file
# postscript(file="Figure2_5p_1b.eps",paper = "special", width=1.25, height=1.5, horizontal=FALSE)
print(ggplot() + geom_logo(seqsAll_5p, method='p') + theme_logo(base_size = 8, base_family = "")) #
# dev.off()
Sample_3pAll <- Sample %>% select(ThreePUMI)
seqsAll_3p <- as.character(Sample_3pAll[!grepl("N",Sample_3pAll$ThreePUMI),])
# save file
# postscript(file="Figure2_3p_1b.eps",paper = "special", width=3, height=1.5, horizontal = FALSE)
print(ggplot() + geom_logo(seqsAll_3p, method='p') + theme_logo(base_size = 8, base_family = ""))
# dev.off()
#
#
# now find motifs for top 25 expressed mature miRNAs
# open counts file
countsdir<-"~/Box Sync/SeqPaperData/TJ12/TJ12_collapse12"
setwd(countsdir)
# Countsfilelist=dir()
filename2="4N23_CB2_1f_1_Mature_count12.txt" #
# filename2=Countsfilelist[seq(1,length(filelist), 50)]
countsfile <- read.table(filename2, header=FALSE, stringsAsFactors = FALSE)
countsfile <- arrange(countsfile, desc(V2))
top25mirs <- countsfile[1:25,1]
#
# > top25mirs
# [1] "hsa-miR-21-5p" "hsa-miR-24-3p" "hsa-let-7a-5p" "hsa-miR-27a-3p" "hsa-miR-100-5p"
# [6] "hsa-miR-191-5p" "hsa-miR-29a-3p" "hsa-miR-16-5p" "hsa-miR-29b-3p" "hsa-miR-125b-5p"
# [11] "hsa-let-7i-5p" "hsa-miR-103b" "hsa-miR-26a-5p" "hsa-let-7f-5p" "hsa-miR-22-3p"
# [16] "hsa-miR-3074-5p" "hsa-miR-96-5p" "hsa-miR-221-3p" "hsa-let-7g-5p" "hsa-miR-23a-3p"
# [21] "hsa-miR-93-5p" "hsa-miR-320a" "hsa-let-7b-5p" "hsa-miR-103a-3p" "hsa-miR-17-5p"
setwd(outdir)
for (mir in top25mirs) {
Sample_mir5p <- Sample %>% filter(miRNA==mir) %>% select(FiveP4nt)
seqs_mir_5p <- as.character(Sample_mir5p[!grepl("N",Sample_mir5p$FiveP4nt),])
motif_file <- paste(mir,"_5p_1b.eps", sep="")
pt= ggplot() + geom_logo( seqs_mir_5p, method='p') + theme_logo(base_size = 8, base_family = "")
# save file
# postscript(file=motif_file,paper = "special", width=1.5, height=1.5, horizontal = FALSE)
print(pt)
# dev.off()
Sample_mir3p <- Sample %>% filter(miRNA==mir) %>% select(ThreePUMI)
seqs_mir_3p <- as.character(Sample_mir3p[!grepl("N",Sample_mir3p$ThreePUMI),])
motif_file2 <- paste(mir,"_3p_1b.eps", sep="")
pt2=ggplot() + geom_logo( seqs_mir_3p, method='p') + theme_logo(base_size = 8, base_family = "")
# save file
# postscript(file=motif_file2, paper = "special", width=3, height=1.5, horizontal = FALSE)
print(pt2)
# dev.off()
}
# now look for the highest one in serum, which is low in the cells
mir = "hsa-miR-96-5p"
Sample_mir5p <- Sample %>% filter(miRNA==mir) %>% select(FiveP4nt)
seqs_mir_5p <- as.character(Sample_mir5p[!grepl("N",Sample_mir5p$FiveP4nt),])
motif_file <- paste(mir,"_5p_2.eps", sep="")
pt= ggplot() + geom_logo( seqs_mir_5p, method='p') + theme_logo(base_size = 8, base_family = "")
# save file
# postscript(file=motif_file,paper = "special", width=1.5, height=1.5, horizontal = FALSE)
print(pt)
# dev.off()
Sample_mir3p <- Sample %>% filter(miRNA==mir) %>% select(ThreePUMI)
seqs_mir_3p <- as.character(Sample_mir3p[!grepl("N",Sample_mir3p$ThreePUMI),])
motif_file2 <- paste(mir,"_3p_2.eps", sep="")
# save file
pt2=ggplot() + geom_logo( seqs_mir_3p, method='p') + theme_logo(base_size = 8, base_family = "")
# postscript(file=motif_file2, paper = "special", width=3, height=1.5, horizontal = FALSE)
print(pt2)
# dev.off()
# serum CB2_1f2
setwd(Seqdir)
outdir<-"~/Box Sync/SeqPaperData/TJ12/motif/CB2_1f2_mature/"
filename="4N24_CB2_1f_2_Mature_count12_seqs.txt"
infoFile=read.table(filename, header=FALSE, stringsAsFactors = FALSE)
# $1=HairpinName, $2=leftSeq/insert, $3=AdapterMatch, $4=WholeSeq, $5=4N-miRNA-4Nbarcode4N
names(infoFile) <- c("miRNA","Insert","AdapterMatch","WholeSeq","TrimmedSeq")
#
Sample <- infoFile %>% mutate(FiveP4nt = (str_sub(infoFile$Insert, 1, 4)), ThreePUMI=(str_sub(AdapterMatch, 1, 13)))
Sample_5pAll <- Sample %>% select(FiveP4nt)
seqsAll_5p <- as.character(Sample_5pAll[!grepl("N",Sample_5pAll$FiveP4nt),])
setwd(outdir)
# save file
# postscript(file="Figure2_5p_2b.eps",paper = "special", width=1.25, height=1.5, horizontal=FALSE)
print(ggplot() + geom_logo(seqsAll_5p, method='p') + theme_logo(base_size = 8, base_family = "")) #
# dev.off()
Sample_3pAll <- Sample %>% select(ThreePUMI)
seqsAll_3p <- as.character(Sample_3pAll[!grepl("N",Sample_3pAll$ThreePUMI),])
# save file
# postscript(file="Figure2_3p_2b.eps",paper = "special", width=3, height=1.5, horizontal = FALSE)
print(ggplot() + geom_logo(seqsAll_3p, method='p') + theme_logo(base_size = 8, base_family = ""))
# dev.off()
#
#
# now find motifs for top 25 expressed mature miRNAs
# open counts file
countsdir<-"~/Box Sync/SeqPaperData/TJ12/TJ12_collapse12"
setwd(countsdir)
# Countsfilelist=dir()
filename2="4N24_CB2_1f_2_Mature_count12.txt" #
# filename2=Countsfilelist[seq(1,length(filelist), 50)]
countsfile <- read.table(filename2, header=FALSE, stringsAsFactors = FALSE)
countsfile <- arrange(countsfile, desc(V2))
top25mirs <- countsfile[1:25,1]
#
# > top25mirs
# [1] "hsa-miR-21-5p" "hsa-miR-24-3p" "hsa-let-7a-5p" "hsa-miR-27a-3p" "hsa-miR-100-5p"
# [6] "hsa-miR-191-5p" "hsa-miR-29a-3p" "hsa-miR-16-5p" "hsa-miR-29b-3p" "hsa-miR-125b-5p"
# [11] "hsa-let-7i-5p" "hsa-miR-103b" "hsa-miR-26a-5p" "hsa-let-7f-5p" "hsa-miR-22-3p"
# [16] "hsa-miR-3074-5p" "hsa-miR-96-5p" "hsa-miR-221-3p" "hsa-let-7g-5p" "hsa-miR-23a-3p"
# [21] "hsa-miR-93-5p" "hsa-miR-320a" "hsa-let-7b-5p" "hsa-miR-103a-3p" "hsa-miR-17-5p"
setwd(outdir)
for (mir in top25mirs) {
Sample_mir5p <- Sample %>% filter(miRNA==mir) %>% select(FiveP4nt)
seqs_mir_5p <- as.character(Sample_mir5p[!grepl("N",Sample_mir5p$FiveP4nt),])
motif_file <- paste(mir,"_5p_2b.eps", sep="")
pt= ggplot() + geom_logo( seqs_mir_5p, method='p') + theme_logo(base_size = 8, base_family = "")
# save file
# postscript(file=motif_file,paper = "special", width=1.5, height=1.5, horizontal = FALSE)
print(pt)
# dev.off()
Sample_mir3p <- Sample %>% filter(miRNA==mir) %>% select(ThreePUMI)
seqs_mir_3p <- as.character(Sample_mir3p[!grepl("N",Sample_mir3p$ThreePUMI),])
motif_file2 <- paste(mir,"_3p_2b.eps", sep="")
# save file
pt2=ggplot() + geom_logo( seqs_mir_3p, method='p') + theme_logo(base_size = 8, base_family = "")
# postscript(file=motif_file2, paper = "special", width=3, height=1.5, horizontal = FALSE)
print(pt2)
# dev.off()
}
# system information at time of this rendering:
sessionInfo()
## R version 3.4.0 (2017-04-21)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: OS X El Capitan 10.11.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggseqlogo_0.0.1 stringr_1.2.0 dplyr_0.5.0 purrr_0.2.2.2
## [5] readr_1.1.1 tidyr_0.6.3 tibble_1.3.3 ggplot2_2.2.1
## [9] tidyverse_1.1.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.11 cellranger_1.1.0 compiler_3.4.0 plyr_1.8.4
## [5] forcats_0.2.0 tools_3.4.0 digest_0.6.12 lubridate_1.6.0
## [9] jsonlite_1.5 evaluate_0.10 nlme_3.1-131 gtable_0.2.0
## [13] lattice_0.20-35 rlang_0.1.1 psych_1.7.5 DBI_0.6-1
## [17] parallel_3.4.0 haven_1.0.0 xml2_1.1.1 httr_1.2.1
## [21] knitr_1.16 hms_0.3 rprojroot_1.2 grid_3.4.0
## [25] R6_2.2.1 readxl_1.0.0 foreign_0.8-68 rmarkdown_1.5
## [29] modelr_0.1.0 reshape2_1.4.2 magrittr_1.5 backports_1.1.0
## [33] scales_0.4.1 htmltools_0.3.6 rvest_0.3.2 assertthat_0.2.0
## [37] mnormt_1.5-5 colorspace_1.3-2 labeling_0.3 stringi_1.1.5
## [41] lazyeval_0.2.0 munsell_0.4.3 broom_0.4.2